Types of visualizations

1 - Line plot

Visualizing median GDP per capita over time

A line plot is useful for visualizing trends over time. In this exercise, you’ll examine how the median GDP per capita has changed over time.

# Load the knitr and kableExtra packages
library(knitr)
library(kableExtra)
options(knitr.table.format = "html")
# Load the gapminder package
library(gapminder)
# Load the dpylr package
library(dplyr)
# Load the ggplot2 package as well
library(ggplot2)
theme_set(theme_bw())  # pre-set the bw theme.
# Summarize the median gdpPercap by year, then save it as by_year
by_year <- gapminder %>%
   group_by(year) %>%
   summarize(medianGdpPercap = median(gdpPercap))

# Create a line plot showing the change in medianGdpPercap over time
ggplot(by_year, aes(x = year, y = medianGdpPercap)) +
   geom_line() + 
   expand_limits(y = 0) +
   labs(subtitle="Median GDP per capita over time", 
        y="GDP per capita", 
        x="Year", 
        title="Line plot", 
        caption = "")

Looks like median GDP per capita across countries has gone up over time.

Visualizing median GDP per capita by continent over time

In the last exercise you used a line plot to visualize the increase in median GDP per capita over time. Now you’ll examine the change within each continent.

# Summarize the median gdpPercap by year & continent, save as by_year_continent
by_year_continent <- gapminder %>%
   group_by(year, continent) %>%
   summarize(medianGdpPercap = median(gdpPercap))

# Create a line plot showing the change in medianGdpPercap by continent over time
ggplot(by_year_continent, aes(x = year, y=medianGdpPercap, color= continent)) +
   geom_line() +
   expand_limits(y = 0) +
   labs(subtitle="Median GDP per capita over time by continent", 
        y="GDP per capita", 
        x="Year", 
        title="Line plot", 
        caption = "")

Did the growth in median GDP per capita differ between continents?

2 - Bar plot

Visualizing median GDP per capita by continent

A bar plot is useful for visualizing summary statistics, such as the median GDP in each continent.

# Summarize the median gdpPercap by year and continent in 1952
by_continent <- gapminder %>%
   filter(year == 1952) %>%
   group_by(continent) %>%
   summarize(medianGdpPercap = median(gdpPercap))

# Create a bar plot showing medianGdp by continent
ggplot(by_continent, aes(x = continent, y = medianGdpPercap)) +
   geom_col() +
   labs(subtitle="Median GDP per capita by continent", 
        y="GDP per capita", 
        x="Continent", 
        title="Bar plot", 
        caption = "")

Visualizing GDP per capita by country in Oceania

You’ve created a plot where each bar represents one continent, showing the median GDP per capita for each. But the x-axis of the bar plot doesn’t have to be the continent: you can instead create a bar plot where each bar represents a country.

In this exercise, you’ll create a bar plot comparing the GDP per capita between the two countries in the Oceania continent (Australia and New Zealand).

# Filter for observations in the Oceania continent in 1952
oceania_1952 <- gapminder %>%
   filter(continent == "Oceania", year == 1952)

# Create a bar plot of gdpPercap by country
ggplot(oceania_1952, aes(x = country, y = gdpPercap)) +
   geom_col() +
   labs(subtitle="GDP per capita by country of Oceania", 
        y="GDP per capita", 
        x="Country", 
        title="Bar plot", 
        caption = "")

3 - Histogram plot

Visualizing population

A histogram is useful for examining the distribution of a numeric variable. In this exercise, you’ll create a histogram showing the distribution of country populations in the year 1952.

Note: geom_histogram() will output a warning that you should pick a better binwidth. Feel free to ignore this here and do not pass a selected binwidth into the function.

# Dataset with country populations in the year 1952
gapminder_1952 <- gapminder %>%
  filter(year == 1952)

# Create a histogram of population (pop)
ggplot(gapminder_1952, aes(x = pop)) +
   geom_histogram() +
   labs(subtitle="Distribution of country populations in the year 1952", 
        y="Count", 
        x="Population", 
        title="Histogram plot", 
        caption = "")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Notice that most of the distribution is in the smallest (leftmost) bins. In the next exercise you’ll put the x-axis on a log scale.

Visualizing population with x-axis on a log scale

In the last exercise you created a histogram of populations across countries. You might have noticed that there were several countries with a much higher population than others, which causes the distribution to be very skewed, with most of the distribution crammed into a small part of the graph. (Consider that it’s hard to tell the median or the minimum population from that histogram).

To make the histogram more informative, you can try putting the x-axis on a log scale.

# Create a histogram of population (pop), with x on a log scale
ggplot(gapminder_1952, aes(x = pop)) +
   geom_histogram() +
   scale_x_log10() +
   labs(subtitle="Distribution of country populations (passed to log) in the year 1952", 
        y="log(count)", 
        x="Population", 
        title="Histogram plot", 
        caption = "")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Notice that on a log scale, the distribution of country populations is approximately symmetrical.

4 - Boxplot

Comparing GDP per capita across continents

A boxplot is useful for comparing a distribution of values across several groups. In this exercise, you’ll examine the distribution of GDP per capita by continent. Since GDP per capita varies across several orders of magnitude, you’ll need to put the y-axis on a log scale.

# Create a boxplot comparing gdpPercap among continents
ggplot(gapminder_1952, aes(x = continent, y = gdpPercap)) +
   geom_boxplot() +
   scale_y_log10() +
   labs(subtitle="Comparing GDP per capita across continents in 1952", 
        y="GDP per capita", 
        x="Continent", 
        title="Boxplot", 
        caption = "")